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Some pictures of different activities during this year 2018
A group of us from Scallable Cooperation group at MediaLab visited Riyad, Abu Dhabi and Dubai to talk about Future of Work.
Complenet happenned in Boston this year. Here I am talking about how do you met your best friend?
I was an invited speaker at NERCCS 2018 conference to talk about our work on the dynamics of strong ties
A bunch of network scientist close to the Netsci 2018 venue

I started using Twitter more than 10 years ago (!). I open an account in this social network in 2008 and although I was not using it too much for the first year, I become a frequent user after that. It has helped me to get news, information both for my personal and professional interests. But not only that, Twitter has been also the data source for our research, that helped us to investigate the relationship between human behavior in the social platform and paramount problems in our society as information propagation, unemployment, disaster damage, political opinion.

Authors: Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey, Peter Krafft, Esteban Moro, Alex `Sandy’ Pentland
Journal: Preprint (2018). arXiv
Abstract: In this empirical paper, we investigate how learning agents can be arranged in more efficient communication topologies for improved learning. This is an important problem because a common technique to improve speed and robustness of learning in deep reinforcement learning and many other machine learning algorithms is to run multiple learning agents in parallel.

Authors: Eaman Jahani, Peter M. Krafft, Yoshihiko Suhara, Esteban Moro, Alex “Sandy” Pentland
Journal: Proc. ACM Hum.-Comput. Interact. 2, CSCW, Article 79 (November 2018), 28 pages. LINK
Abstract: Participants in cryptocurrency markets are in constant communication with each other about the latest coins and news releases. Do these conversations build hype through the contagiousness of excitement, help the community process information, or play some other role? Using a novel dataset from a major cryptocurrency forum, we conduct an exploratory study of the characteristics of online discussion around cryptocurrencies.

Authors: Marcin Waniek, Kai Zhou, Yevgeniy Vorobeychik, Esteban Moro, Tomasz P Michalak, Talal Rahwan
Journal: Preprint (2018). arXiv
Abstract: Link prediction is one of the fundamental research problems in network analysis. Intuitively, it involves identifying the edges that are most likely to be added to a given network, or the edges that appear to be missing from the network when in fact they are present. Various algorithms have been proposed to solve this problem over the past decades.

Authors: David Garcia, Yonas Mitike Kassa, Angel Cuevas, Manuel Cebrian, Esteban Moro, Iyad Rahwan, and Ruben Cuevas
Journal: PNAS June 19, 2018. 201717781. LINK
Abstract: Online social media are information resources that can have a transformative power in society. While the Web was envisioned as an equalizing force that allows everyone to access information, the digital divide prevents large amounts of people from being present online. Online social media, in particular, are prone to gen- der inequality, an important issue given the link between social media use and employment.

Authors: Patrick Baylis, Nick Obradovich, Yury Kryvasheyeu, Haohui Chen, Lorenzo Coviello, Esteban Moro, Manuel Cebrian, James H. Fowler
Journal: PLoS ONE 13(4): e0195750 (2018) LINK
Abstract: We conduct the largest ever investigation into the relationship between meteorological con- ditions and the sentiment of human expressions. To do this, we employ over three and a half billion social media posts from tens of millions of individuals from both Facebook and Twitter between 2009 and 2016.

Authors: Abhimanyu Dubey, Esteban Moro, Manuel Cebrian, Iyad Rahwan
Journal: WWW’18 Proceedings of the Web Conference 2018 LINK
Abstract: The analysis of the creation, mutation, and propagation of social media content on the Internet is an essential problem in computational social science, affecting areas ranging from marketing to political mobilization. A first step towards understanding the evolution of images online is the analysis of rapidly modifying and propagating memetic imagery or ‘memes’.